Estimating value at risk: LSTM vs. GARCH

• URL: http://arxiv.org/abs/2207.10539v1
• Date: Thu, 21 Jul 2022 15:26:07 GMT
• Title: Estimating value at risk: LSTM vs. GARCH
• Authors: Weronika Ormaniec, Marcin Pitera, Sajad Safarveisi, Thorsten Schmidt
• Abstract summary: We propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.

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